Apparatus and method for processing information and program for the same

ABSTRACT

In a first aspect of the present invention, provided are an information processing apparatus including a behavior-history acquisition unit configured to acquire behavior histories of first users identified by first-user identification information, a transmission-history acquisition unit configured to acquire information transmission histories of second users identified by second-user identification information, and a determination unit configured to determine identity between the first users and the second users on the basis of behavior details included in the behavior histories and transmission details included in the transmission histories; a method for processing information with the information processing apparatus; and a program using the information processing apparatus.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. §119from Application No. 2013-246493, filed on Nov. 28, 2013 in Japan.

BACKGROUND

The present invention relates to an apparatus and a method forprocessing information and a program for the same.

A method for identifying users in a plurality of social network services(SNSs) is known in the related art (for example, Patent Literature 1).

CITATION LIST

-   [Patent Literature 1] Japanese Unexamined Patent Application    Publication No. 2013-122630.

SUMMARY

However, there is no known method for specifying identity between usersby associating transmission histories of users in SNSs or the like withactual behaviors of users, such as product purchase.

In a first aspect of the present invention, provided are an informationprocessing apparatus including a behavior-history acquisition unitconfigured to acquire behavior histories of first users identified byfirst-user identification information, a transmission-historyacquisition unit configured to acquire information transmissionhistories of second users identified by second-user identificationinformation, and a determination unit configured to determine identitybetween the first users and the second users on the basis of behaviordetails included in the behavior histories and transmission detailsincluded in the transmission histories; a method for processinginformation with the information processing apparatus; and a programusing the information processing apparatus.

Note that the outline of the present invention described above includenot all necessary features of the present invention and that asub-combination of these features can also be the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the configuration of an informationprocessing apparatus 10 of an embodiment.

FIG. 2 is a flowchart for the process of the information processingapparatus 10 of the embodiment.

FIG. 3 is a diagram illustrating an example of a transmission history ofthe embodiment.

FIG. 4 is a diagram illustrating an example of a behavior history ofthis embodiment.

FIG. 5 is a diagram illustrating examples of the degree of similaritythat a degree-of-similarity calculation section 114 calculates in thisembodiment.

FIG. 6 is a diagram illustrating examples of the degrees of associationthat a degree-of-association calculation section 112 calculates in thisembodiment.

FIG. 7 is a diagram illustrating an example of the hardwareconfiguration of a computer 1900.

DETAILED DESCRIPTION

Although the present invention will be described hereinbelow based onembodiments, it is to be understood that the embodiments do not limitthe scope of claims of the present invention. Not all combinations offeatures described in the embodiments are absolutely necessary for thesolutions of the present invention.

FIG. 1 illustrates the configuration of an information processingapparatus 10 of an embodiment. The information processing apparatus 10acquires a behavior history, such as a product purchase history, from anexternal server 20 and acquires an SNS posting transmission history froman external server 30. Here, the service of the external server 20 andthe service of the external server 30 may be separately provided.Therefore, one and the same person registers the service of the externalserver 20 and the service of the external server 30 separately and isprovided with separate log-in IDs or the like.

The information processing apparatus 10 detects whether the IDs of suchseparately registered services are of one and the same person. Theinformation processing apparatus 10 includes a behavior-historyacquisition unit 102, a transmission-history acquisition unit 104, adetermination unit 110, a degree-of-importance calculation unit 120, anda delivery unit 130.

The behavior-history acquisition unit 102 acquires behavior histories offirst users identified by first-user identification information. Forexample, the behavior-history acquisition unit 102 acquires behaviorhistories, such as product purchase histories, for a plurality of firstusers identified by the first-user identification information, such aslog-in IDs in an on-line shopping service or the like, that the externalserver 20 provides, from the external server 20. The behavior-historyacquisition unit 102 provides the acquired behavior histories to thedetermination unit 110.

The transmission-history acquisition unit 104 acquires informationtransmission histories of second users identified by second-useridentification information. For example, the transmission-historyacquisition unit 104 acquires message transmission histories of aplurality of second users identified by second-user identificationinformation, such as log-in IDs in SNS services provided by the externalserver 30, from the external server 30. The transmission-historyacquisition unit 104 provides the acquired transmission histories to thedetermination unit 110.

The determination unit 110 determines the identity between the firstusers and the second users on the basis of behavior details included inthe behavior histories and transmission details included in thetransmission histories. For example, the determination unit 110calculates the degrees of association of combinations of a behaviorhistory of each first user and a transmission history of each seconduser also on the basis of behavior details included in the behaviorhistories and behavior timing and transmission details included in thetransmission histories and transmission timing and determines theidentity between the first users and the second users on the basis ofthe plurality of degrees of association of the plurality ofcombinations. The determination unit 110 includes adegree-of-association calculation section 112, a degree-of-similaritycalculation section 114, and an identical-user extraction section 116.

The degree-of-association calculation section 112 calculates the degreeof association for each of combinations of a behavior history of eachfirst user and a transmission history of each second user. Furthermore,the degree-of-association calculation section 112 may calculate adifference indicating the degree of difference between each first userand each second user for each of combinations of a behavior history ofeach first user and a transmission history of each second user. Thedegree-of-association calculation section 112 provides the calculateddegrees of association and the degrees of difference to thedegree-of-similarity calculation section 114.

The degree-of-similarity calculation section 114 calculates the degreeof similarity for each of combinations of a first user and a second useron the basis of the plurality of degrees of association and so on. Thedegree-of-similarity calculation section 114 provides the calculateddegrees of similarity to the identical-user extraction section 116.

The identical-user extraction section 116 extracts combinations of afirst user and a second user whose similarity is a predeterminedthreshold or greater from among the combinations of a first user and asecond user. The identical-user extraction section 116 provides theextracted combinations of each first user and each second user to thedegree-of-importance calculation unit 120.

The degree-of-importance calculation unit 120 calculates the degree ofimportance of a user specified from a first user and a second usercorresponding to a combination of the first user and the second user onthe basis of the degree of similarity between the first user and thesecond user and the degree of influence of transmission of informationfrom the second user included in the combination. For example, thedegree-of-importance calculation unit 120 highly estimates the degree ofimportance of a user specified from a combination of a first user and asecond user whose degree of similarity and degree of influence oftransmission are high. The degree-of-importance calculation unit 120provides the calculated degrees of importance of the user to thedelivery unit 130.

The delivery unit 130 delivers information to users whose degree ofimportance satisfies a predetermined condition. For example, thedelivery unit 130 may deliver direct mail related to a product or thelike to first users whose degree of importance is a threshold value orgreater.

In this way, the information processing apparatus 10 can extract acombination of a first user and a second user who may be an identicaluser by associating the first users included in behavior histories andthe second users included in transmission histories using their behaviordetails and transmission details. Furthermore, the informationprocessing apparatus 10 can effectively transmit information to firstusers who may have a high degree of influence.

FIG. 2 is a flowchart for the process of the information processingapparatus 10 of this embodiment. The information processing apparatus 10executes a method for processing information according to thisembodiment by performing the process from S110 to S180.

First, in S110, the behavior-history acquisition unit 102 acquiresbehavior histories of first users identified by the first-useridentification information from the external server 20. For example, thebehavior-history acquisition unit 102 may acquire product or servicepurchase histories as the behavior histories of the first users via theInternet. In an example, the behavior-history acquisition unit 102 mayacquire, for a plurality of first users, a set of first identificationinformation, such as a user ID, and a product purchase history includinga purchased-product name, a purchased-product price, a purchased-productcategory, a purchased-product destination address, and purchase date andtime. The behavior-history acquisition unit 102 may acquire behaviorhistories from a plurality of external servers 20 for a plurality ofsame/different services.

Furthermore, the behavior-history acquisition unit 102 may acquire firstprofile information on first users from an electronic commerce (EC) siteor the like. For example, the behavior-history acquisition unit 102 mayacquire, as the first profile information, user profiles each includingthe first identification information, name, mail address, address,and/or taste, and the like of a first user. The behavior-historyacquisition unit 102 provides the acquired behavior histories and so onto the determination unit 110.

Next, in S120, the transmission-history acquisition unit 104 acquiresinformation transmission histories of second users identified bysecond-user identification information from the external server 30. Forexample, the transmission-history acquisition unit 104 may acquireposting histories of the second users, which are histories of posting ofinformation to a Website that receives the posting using the second-useridentification information. The transmission-history acquisition unit104 may acquire transmission histories from a plurality of externalservers 30 for a plurality of same/different services.

In an example, the transmission-history acquisition unit 104 mayacquire, as transmission histories of a plurality of second users, thesecond-user identification information, such as user IDs, texttransmitted on the Internet, text, sound, images, and/or moving imagesposted to a Website. Here, the transmission-history acquisition unit 104may acquire the posted text, text extracted from the posted sound,and/or text analyzed from the posted images or moving images asinformation transmission histories of the second users.

Here, the transmission-history acquisition unit 104 may acquiretransmission histories obtained by searching for original transmissionhistories with another external server. For example, thetransmission-history acquisition unit 104 may acquire transmissionhistories extracted by searching for transmission histories, which areopened on the Internet by the external server 30, with a search serverof a Web search engine.

In an example, the transmission-history acquisition unit 104 mayextract, from a plurality of transmission histories, a transmissionhistory including information related to a behavior detail orinformation on a product or service related to the behavior history, forexample, a history including a keyword indicating that a product orservice related to the behavior history was purchased.

Specifically, in the case where the behavior-history acquisition unit102 acquires product purchase histories on the Internet, thetransmission-history acquisition unit 104 may searches for transmissionhistories using keywords related to product purchase on the Internet,such as “buy”, “purchase”, “sale”, “reserve”, “distribute”, “deliver”,and/or “the name of a service that the external server 20 provides (forexample, the name of a service that an EC site provides)” to search fortransmission histories including the keywords.

Furthermore, the transmission-history acquisition unit 104 may acquiresecond profile information on second users from a Website serving as aposting destination. For example, the transmission-history acquisitionunit 104 may acquire, as second profile information, user profiles eachincluding the second identification information, mail address, name,address, community to which the second user belongs, and/or taste of thesecond user.

Furthermore, the transmission-history acquisition unit 104 may acquireinformation on the degrees of influence of second users from a Website(for example, the numbers of followers and/or friends of the secondusers in the Website from the Website). The transmission-historyacquisition unit 104 may provide the transmission histories and profileinformation of a plurality of second users to the determination unit 110and may provide information on the degrees of influence of the pluralityof second users to the degree-of-importance calculation unit 120.

Next, in S130, the degree-of-association calculation section 112calculates the degree of association for each of combinations of abehavior history of a first user and a transmission history of a seconduser. For example, first, the degree-of-association calculation section112 calculates the degree of agreement q(x(id,n),y(ID,N)) so that thedegree becomes higher as the degree of agreement between a behaviordetail x(id,n) in the behavior history of a first user id having firstidentification information id and a transmission detail in thetransmission history of a second user ID having the extracted secondidentification information ID increases.

In an example, if a behavior detail in a behavior history is that “afirst user bought a specific product (for example, a product XXX) in aspecific EC site (for example, an on-line ship ABC)”, and if atransmission detail in a transmission history is “text posted by asecond user by clicking a posting button (for example, a tweet button ora like button) in an SNS and by posting a comment that the second userbought the specific product (for example, the product XXX) in thespecific EC site (for example, the on-line ship ABC), thedegree-of-association calculation section 112 may estimate the degree ofagreement to be 1.

In another example, if a behavior detail in the behavior history is that“a first user bought a specific product (for example, a product XXX) ina specific EC site (for example, an on-line shop ABC)”, and if atransmission detail in the transmission history is “text including theURL of the specific product (for example, the product XXX) in thespecific EC site (for example, the on-line shop ABC) and a word closelyrelated to purchase, such as “purchase”, “buy”, “bought”, “distribution”or “deliver”, the degree-of-association calculation section 112 mayestimate the degree of agreement to be 0.8.

In another example, if a behavior detail in the behavior history is that“a first user bought a specific product (for example, a product XXX) ina specific EC site (for example, an on-line shop ABC), and if atransmission detail in the transmission history is “text including apartial character string of the name of the specific product (forexample, the product XXX) and a word related to purchase, such as“purchase”, “buy”, “bought”, “received”, “distribution” or “deliver”,the degree-of-association calculation section 112 may estimate thedegree of agreement to be 0.6.

In another example, if a behavior detail in the behavior history is that“a first user bought a specific product (for example, a product XXX) ina specific EC site (for example, an on-line shop ABC), and if atransmission detail in the transmission history is “text including apartial character string of the name of the specific product (forexample, the product XXX), the degree-of-association calculation section112 may estimate the degree of agreement to be 0.5.

In another example, if a behavior detail in the behavior history is that“a first user bought a specific product (for example, a product XXX) ina specific EC site (for example, an on-line shop ABC), and if atransmission detail in the transmission history is “text including thecategory name (for example, “book”, “music”, and/or “daily product”) ofthe specific product (for example, the product XXX) in the specific ECsite (for example, the on-line shop ABC), the degree-of-associationcalculation section 112 may estimate the degree of agreement to be 0.2.

The degree-of-association calculation section 112 may assign the degreeof agreement in consideration of the time and difference between thedate and time of a behavior in a behavior history and the date and timeof transmission in a transmission history. For example, if thetransmission date and time is after the behavior date and time, thedegree-of-association calculation section 112 may assign a higher degreeof agreement than that when transmission date and time is before thebehavior date and time. For example, if the difference between thetransmission date and time and the behavior date and time is within oneday, the degree-of-association calculation section 112 may multiply thedegree of agreement by 1; if the difference is within two days, maymultiply the degree of agreement by 0.8; if the degree is within threedays, may multiply the degree of agreement by 0.5; if the difference iswithin one week, may multiply the degree of agreement by 0.2; and if thedifference is one week or more, may multiply the degree of agreement by0. Here, in the case where the price of a purchased product in thebehavior history is high, the degree-of-association calculation section112 may set the reference difference between the transmission date andtime and the behavior date and time longer than that when the productprice is low.

Alternatively, the degree-of-association calculation section 112 maycalculate the degree of agreement of each of combinations of a behaviordetail in the behavior history and a transmission detail in atransmission history using logistic regression. For example, thedegree-of-association calculation section 112 may assign the degree ofagreement in consideration of (i) whether the transmission detail in thetransmission history includes a partial character string of the name ofthe shop, (ii) whether the transmission detail in the transmissionhistory includes the URL of a bought product, (iii) whether thetransmission detail in the transmission history includes a partialcharacter string of the name of the bought product, (iv) whether thetransmission detail in the transmission history includes a word relatedto purchase, such as “purchase” and “received, and (v) the timedifference between the behavior date and time and the transmission dateand time. In an example, the degree-of-association calculation section112 may calculate the degree of agreement using the conditions (i) to(v) as features so that the degree of agreement increases to the rangeof 0 to 1 as the behavior detail and the behavior detail agreeincreasingly.

Next, in S120, the degree-of-association calculation section 112calculates the degree of association λ(x(id,n),y(ID,N)) between thebehavior detail x(id,n) in the behavior history and the transmissiondetail y(ID,N) in the transmission history on the basis of thecalculated degree of agreement q(x(id,n),y(ID,N)).

The degree-of-association calculation section 112 may calculate thedegree of association λ(x,y) using Eq. 1.

λ(x,y)=1+(r−1)q(x,y)  Eq. 1

where r is a coefficient exceeding 1, which is given to the whole of thebehavior history and the transmission history and is a weight that thedegree of agreement q gives to the degree of association λ. For example,the degree-of-association calculation section 112 may use apredetermined real number r or a real number r determined bycross-validation using part of the behavior history and the transmissionhistory or test data.

Next, in S140, the degree-of-association calculation section 112calculates the degree of difference κ(x(id,n),y(ID,N)) indicating thedegree of difference between a first user id and a second user ID foreach of combinations of the behavior history n of each first user id andthe transmission history N of each second user ID.

For example, first, the degree-of-association calculation section 112calculates the degree of disagreement q′(x(id,n),y(ID,N)) indicating thedegree of disagreement between the behavior detail x(id,n) in thebehavior history of a first user and the transmission detail y(ID,N) inthe transmission history of a second user for each of combinations of abehavior detail in the behavior history and a transmission detail in thetransmission history.

In an example, if a transmission detail in a transmission historyincludes a description on a place name and the difference between thetransmission date and time in the transmission history and the behaviordate and time in the behavior history is within a predetermineddifference, the degree-of-association calculation section 112 maycalculate a degree of disagreement ranging from 0 to 1 so that the valueincreases as the distance between a product destination address includedin the behavior history and the place name included in the transmissiondetail increases.

In another example, if the transmission detail in the transmissionhistory includes at least part of the product name and at least part ofa comment contradicting purchase, such as “want” or “jealous”, thedegree-of-association calculation section 112 may calculate a degree ofdisagreement ranging from 0 to 1 so that the value increases as theproportion of agreement between the transmission detail and the productname and the proportion of agreement between the transmission detail andthe comment contradicting the purchase increase.

Next, the degree-of-association calculation section 112 calculates thedegree of difference κ(x(id,n),y(ID,N)) between the behavior history nand the transmission history N on the basis of the calculated degree ofdisagreement q′(x(id,n),y(ID,N)).

The degree-of-association calculation section 112 may calculate thedegree of difference κ(x,y) using Eq. 2.

x(x,y)=1+(r′−1)q′(x,y)  Eq. 2

where r′ is a coefficient less than 1, which is given to the whole ofbehavior history and the transmission history and is a weight that thedegree of disagreement q′ gives to the degree of difference κ. Forexample, the degree-of-association calculation section 112 may use apredetermined real number r′ or a real number r′ determined bycross-validation using part of the behavior history and the transmissionhistory or test data.

Next, in S150, the degree-of-similarity calculation section 114calculates the degree of similarity for each of combinations of eachfirst user id and each second user ID on the basis of the result ofaccumulation of the degrees of association λ calculated in S130 and thedegrees of difference κ calculated in S140. The degree-of-similaritycalculation section 114 may further calculate the degree of similarityon the basis of the profile information on the first users and theprofile information on the second users. For example, thedegree-of-similarity calculation section 114 may calculate the degree ofsimilarity S(id,ID) between the first user id and the second user IDusing Eq. 3.

$\begin{matrix}{{S\left( {{id},{ID}} \right)} = {\left( {\prod\limits_{n}{\prod\limits_{N}\; {\lambda \left( {{x\left( {{id},n} \right)},{y\left( {{ID},N} \right)}} \right)}}}\; \right) \left( {\prod\limits_{n}{\prod\limits_{N}\; {k\left( {{x\left( {{id},n} \right)},{y\left( {{ID},N} \right)}} \right)}}} \right){p\left( {{id},{ID}} \right)}}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

where p(id,ID) is a profile similarity indicating the degree ofsimilarity between the first profile information on the first user idand the second profile information on the second user ID.

For example, the degree-of-similarity calculation section 114 may setthe profile similarity p(id,ID) high when the prefecture of an addressin the first profile information on the first user id and the prefectureof an address included in the second profile information on the seconduser ID agree. Alternatively, the degree-of-similarity calculationsection 114 may set the profile similarity p(id,ID) high when acharacter string included both in a mail address in the first profileinformation and in an account character string of the second user ID ispresent.

Alternatively, for example, the degree-of-similarity calculation section114 may set the profile similarity p(id,ID) using logistic regression onthe basis of whether the profile information on the first user id andthe profile information on the second user ID include (i) the sameprefecture, (ii) a common character string between the firstidentification information or the mail address of the first user id andthe second identification information or the mail address of the seconduser ID, and/or (iii) a common character string.

The degree-of-similarity calculation section 114 may calculate theprofile similarity p(id,ID) also on the basis of the degree of similarlybetween information on the first user id (for example, the log-in stateand/or log-in frequency of the first user) other than the first profileinformation and information on the second user ID (for example, thelog-in state and/or log-in frequency of the second user) other than thesecond profile information. The degree-of-similarity calculation section114 may set the profile similarity p(id,ID)=1 in the case wheresufficient information cannot be obtained from the profile information,such as when information more than a predetermined amount cannot beobtained from at least one of the first profile and the second profile.

Next, in S160, the identical-user extraction section 116 specifies acombination of a first user id and a second user ID who may be anidentical user at high possibility. For example, the identical-userextraction section 116 extracts a combination of a first user id and asecond user ID whose degree of similarity is a predetermined thresholdvalue or greater from combinations of a first user id and a second userID. The identical-user extraction section 116 provides the extractedcombination of a first user id and a second user ID to thedegree-of-importance calculation unit 120.

Next, in S170, the degree-of-importance calculation unit 120 calculatesthe degree of importance of the user of the combination specified as anidentical user. For example, the degree-of-importance calculation unit120 calculates the degree of influence E of the second user ID accordingto information on the degree of influence of the second user ID (forexample, the number of followers and/or friends of the second user ID).

In an example, the degree-of-importance calculation unit 120 may set thedegree of influence E to 1 (follower: 1,000 or more), 0.8 (follower: 100to less than 1,000), 0.5 (follower: 20 or more to less than 100), 0.2(follower: 10 or more to less than 20), or 0 (follower: less than 10)depending on the number of followers of the second user ID.

Next, the degree-of-importance calculation unit 120 calculates thedegree of importance I(id,ID) of the first user id and the second userID by multiplying the degree of similarity S(id,ID) of the combinationof the first user id and the second user ID by the degree of influenceE(ID) of the second user ID. The degree-of-importance calculation unit120 provides the calculated degree of importance I(id,ID) to thedelivery unit 130.

Next, in S180, the delivery unit 130 delivers information to a firstuser id who is determined to be important. For example, the deliveryunit 130 may transmit, to a first user id whose degree of importanceI(id,ID) is a predetermined threshold value or greater, direct mail,advertisement, and/or samples of products related to a product that asecond user ID corresponding to the first user id bought.

In this way, the information processing apparatus 10 calculates thedegree of association q and the degree of difference q′ for each ofcombinations of a behavior history and a transmission history,calculates the degree of similarity S(id,ID) of the first and secondusers id and ID on the basis of the accumulated degrees of associationand degrees of difference of combinations of a first user id and asecond user ID, and specifies a combination of a first user id and asecond user ID having a high degree of similarity S(id,ID) as acombination who may be an identical user at high possibility. In thisway, the information processing apparatus 10 can achieve nameidentification of a user in a behavior history and a user in atransmission history.

Furthermore, the information processing apparatus 10 determines that afirst user id having a high degree of influence among users specified tobe identical is a user having a high degree of importance andselectively transmits information to the user having the high degree ofimportance, and thus can advertise products and so on to the user.

In a first modification of this embodiment, the degree-of-associationcalculation section 112 may omit the process in S140. In this case, inS150, the degree-of-similarity calculation section 114 may calculate thedegree of similarity S(id,ID) using Eq. 3 in which ΠΠκ(x,y) is removedfrom the right side thereof.

In a second modification of this embodiment, in S140, thedegree-of-association calculation section 112 may calculate the degreeof disagreement q″(λ(id,n),y(ID,N)) for each of combinations of a firstuser id and a transmission detail y(ID,N) in the transmission historyinstead of calculating the degree of disagreement q′(x(id,n),y(ID,N))for each of combinations of the behavior detail x(id,n) of the firstuser id and the transmission details y(ID,N) of the second user ID.

For example, the transmission detail of the second user ID refers to apurchased product or the like; however, if there is no product or thelike corresponding to the purchase history of the first user id, thedegree-of-association calculation section 112 may assign a high degreeof disagreement q″(λ(id,n),y(ID,N)) to a combination of a first user idand a comment N of the second user ID.

In a third modification of this embodiment, the information processingapparatus 10 may use a transportation use history as a behavior historyinstead of the purchase history of a product or the like. For example,in S110, the behavior-history acquisition unit 102 acquires atransportation use history as a behavior history. In an example, thebehavior-history acquisition unit 102 may acquire use histories,including get-on and get-off stations, of a plurality of users from ause-history database server of an external transportation.

In S130, the degree-of-association calculation section 112 may assignthe degree of agreement q(x(id,n),y(ID,N)) to each of combinations ofthe transportation use detail x(id,n) of the first user id and thetransmission detail y(N,ID) of the second user ID. For example, if atransportation use history includes a word of a get-on or get-offstation or part thereof, the word of the place name of a get-on orget-off station or part thereof, or the word of a line name supposed tobe used or part thereof, the degree-of-association calculation section112 may set the degree of agreement so as to increase in this order.

In a fourth modification of this embodiment, the information processingapparatus 10 may calculate the degrees of association between behaviorhistories and transmission histories in advance on the basis of theacquired behavior histories and transmission histories to specify acombination of a first user and a second user.

The information processing apparatus 10 of this modification may acquirea new behavior history and transmission history in addition to theacquired behavior histories and transmission histories as the needarises and may calculate the degree of association (and the degree ofdifference) between the new behavior history and transmission historyand the acquired behavior histories and transmission histories, therebyupdating the degree of similarity between the first users and the secondusers to thereby update the combinations of the first user and thesecond user.

In this case, in S130, the degree-of-association calculation section 112may calculate the coefficient r in Eq. 1 on the basis of Eq. 4.

$\begin{matrix}{r = \frac{\frac{\sum\limits_{id}\; {m({id})}}{\sum\limits_{id}\; {M({id})}}}{\frac{\sum\limits_{id}{{n({id})}{N({id})}}}{\left( {\sum\limits_{id}{n({id})}} \right)\left( {\sum\limits_{id}{N({id})}} \right)}}} & {{Eq}.\mspace{14mu} 4}\end{matrix}$

where id in Eq. 4 may be identification information provided to acombination of a first user and a second user determined to be anidentical user from the acquired behavior histories and transmissionhistories; for example, the id of an identical user 1 is given to afirst combination of a first user id and a second user ID, and the id ofan identical user 2 is given to a second combination of a first user idand a second user ID.

Here, n(id) is the number of behavior histories related to the identicaluser id (for example, the number of purchases), and Σn(id) is the sum ofthe number of behavior histories of all the identical users. N(id) isthe number of transmission histories related to the identical user id(for example, the number of comments posted on an SNS), and ΣN(id) isthe sum of the number of transmission histories of all the identicalusers.

Here, m(id) is the number of transmission details related to any of thebehavior details in the behavior history of an identical user among thetransmission details in transmission histories related to the identicalusers id (for example, transmission details whose calculated degrees ofassociation are a predetermined threshold value or greater, and Σm(id)is the sum of the numbers m(id) of all the identical users.

M(id) is the number of transmission details related to any of thebehavior histories of all the identical users among the transmissiondetails in the transmission histories of the identical users id (forexample, transmission details whose calculated degrees of associationare a predetermined threshold value or greater), and ΣM(id) is the sumof the numbers M(id) of all the identical users. Σn(id)N(id) is the sumof the numbers n(id)×N(id) of all the identical users id.

In S140, the degree-of-association calculation section 112 of thismodification may calculate r′ on the basis of Eq. 5.

$\begin{matrix}{r^{\prime} = \frac{\frac{\sum\limits_{id}\; {m^{\prime}}^{({id})}}{\sum\limits_{id}\; {M^{\prime}}^{({id})}}}{\frac{\sum\limits_{id}{{n^{\prime}}^{({id})}{N^{\prime}}^{({id})}}}{\left( {\sum\limits_{id}{n^{\prime}({id})}} \right)\left( {\sum\limits_{id}{N^{\prime}({id})}} \right)}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

where id, n′(id), and N′(id) in Eq. 5 may be the same as id, n(id), andN(id) in Eq. 4.

Where, m′(id) is the number of transmission details determined to becontradictory to any of the behavior details in the behavior history ofan identical user among the transmission details in transmissionhistories related to the identical user id (for example, transmissiondetails whose calculated degrees of difference are a predeterminedthreshold value or greater), and Σm′(id) is the sum of the numbersm′(id) of all the identical users. M′(id) is the number of transmissiondetails determined to be contradictory to any of the behavior details ofan identical user among the transmission details related to theidentical user id (for example, transmission details whose calculateddegrees of difference are a predetermined threshold value or greater),and ΣM′(id) is the sum of numbers M′(id) of all the identical users.

FIG. 3 illustrates an example of a transmission history according tothis embodiment. The transmission-history acquisition unit 104 mayacquire a history of posting to SNS, such as a microblogging site. Forexample, as shown in FIG. 3, the transmission history that thetransmission-history acquisition unit 104 acquires includes the secondidentification information (for example, “yamadataro”), posted comments,posting dates and times of the second users. The posted comments includeinformation, such as shops at which the second users bought products(“on-line shop ABC” and “ABC”), and purchased product information (“CD”,“a band XXX”, and “a XXX limited disc”).

FIG. 4 illustrates an example of the behavior history of thisembodiment. The behavior-history acquisition unit 102 may acquire apurchase history in an EC site. For example, as shown in FIG. 4, thebehavior-history acquisition unit 102 acquires the first identificationinformation on the first users (for example, “Taro Yamada”), productsthat the first users bought (“a book AAA” etc.), addresses (“AAprefecture AA” etc.), purchase dates, and delivery dates.

FIG. 5 illustrates examples of the degree of similarity that thedegree-of-similarity calculation section 114 calculates in thisembodiment. As shown in FIG. 5, the degree-of-similarity calculationsection 114 calculates the degrees of similarity of combinations of aplurality of first users A to D and a plurality of second users A to D.For example, the degree-of-similarity calculation section 114 calculatesthe degree of similarity AA for a combination of the first user A andthe second user A, the degree of similarity AB for a combination of thefirst user A and the second user B, the degree of similarity AC for acombination of the first user A and the third user C, and the degree ofsimilarity AD for a combination of the first user A and the second userD. In this way, the degree-of-similarity calculation section 114calculates the degree of similarity for all combinations of all firstusers and all second users.

FIG. 6 illustrates examples of the degree of association that thedegree-of-association calculation section 112 calculates in thisembodiment. As shown in FIG. 6, the degree-of-association calculationsection 112 calculates the degree of association for each ofcombinations of a plurality of behavior histories A to D of the firstuser A and a plurality of transmission histories A to D of a second userA. For example, the degree-of-association calculation section 112calculates the degree of association AA for a combination of thebehavior history A of the first user A and the transmission history A ofthe second user A, the degree of association AB for a combination of thebehavior history A of the first user A and the transmission history B ofthe second user A, the degree of association AC for a combination of thebehavior history A of the first user A and the transmission history C ofthe second user A, and the degree of association AD for a combination ofthe behavior history A of the first user A and the transmission historyD of the second user A. As described above, the degree-of-associationcalculation section 112 calculates the degree of association for all ofcombinations of all the behavior histories of one first user A and allthe transmission histories of one second user A.

FIG. 7 illustrates an example hardware configuration of a computer 1900serving as the information processing apparatus 10. The computer 1900according to this embodiment includes CPU peripherals including a CPU2000, a RAM 2020, and a graphic controller 2075, and a display device2080 which are mutually connected by a host controller 2082; aninput/output section including a communication interface 2030, a harddisk drive 2040, an input/output controller 2084, and a CD-ROM drive2060 which are connected to the host controller 2082 by an input/outputcontroller 2084; and a legacy input/output section including a ROM 2010,a flexible disk drive 2050, and an input/output chip 2070 which areconnected to the input/output controller 2084.

The host controller 2082 connects the RAM 2020, the CPU 2000 thataccesses the RAM 2020 at a high transfer rate, and the graphiccontroller 2075 together. The CPU 2000 operates on the basis of aprogram stored in the ROM 2010 and the RAM 2020 to control thecomponents.

The graphic controller 2075 acquires image data that the CPU 2000 and soon generate on a frame buffer provided in the RAM 2020 and displays thedata on the display device 2080. Alternatively, the graphic controller2075 may include a frame buffer that stores image data generated by theCPU 2000 and so on.

The input/output controller 2084 connects the host controller 2082 withthe communication interface 2030, the hard disk drive 2040, and theCD-ROM drive 2060, which are relatively high-speed input/output devices.The communication interface 2030 communicates with another device via anetwork by wire or wirelessly.

The communication interface 2030 functions as hardware for communicationin the information processing apparatus 10. The hard disk drive 2040stores a program and data that the CPU 2000 in the computer 1900 uses.

The CD-ROM drive 2060 reads a program or data from a CD-ROM 2095 andprovides it to the hard disk drive 2040 via the RAM 2020.

The input/output controller 2084 connects to the ROM 2010, the flexibledisk drive 2050, and the input/output chip 2070, which are relativelylow-speed input/output devices. The ROM 2010 stores a boot program thatthe computer 1900 executes at startup and/or a program that depends onthe hardware of the computer 1900, and so on.

The flexible disk drive 2050 reads a program or data from a flexibledisk 2090 and provides it to the hard disk drive 2040 via the RAM 2020.The input/output chip 2070 connects the flexible disk drive 2050 to theinput/output controller 2084 and connects various input-output devicesto the input/output controller 2084 via a parallel port, a serial port,a keyboard port, or a mouse port.

The program provided to the hard disk drive 2040 via the RAM 2020 isstored in a storage medium, such as the flexible disk 2090, the CD-ROM2095, or an IC card, and is provided by a user. The program is read fromthe storage medium, is installed in the hard disk drive 2040 in thecomputer 1900 via the RAM 2020, and is executed in the CPU 2000.

The program installed in the computer 1900 for causing the computer 1900to function as the information processing apparatus 10 includes abehavior-history acquisition module, a transmission-history acquisitionmodule, a determination module, a degree-of-association calculationmodule, a degree-of-similarity calculation module, an identical-userextraction module, a degree-of-importance calculation module, and adelivery module. These program and modules may work the CPU 2000 and soon so that the computer 1900 functions as the behavior-historyacquisition unit 102, the transmission-history acquisition unit 104, thedetermination unit 110, the degree-of-association calculation section112, the degree-of-similarity calculation section 114, theidentical-user extraction section 116, the degree-of-importancecalculation unit 120, and the delivery unit 130.

Information processing described in the program is read by the computer1900 and functions as the behavior-history acquisition unit 102, thetransmission-history acquisition unit 104, the determination unit 110,the degree-of-association calculation section 112, thedegree-of-similarity calculation section 114, the identical-userextraction section 116, the degree-of-importance calculation unit 120,and the delivery unit 130, which are specific units in which softwareand the various hardware resources described above cooperate. Theinformation processing apparatus 10 suitable for intended use isconfigured by these specific means implementing calculation orprocessing of information according to the intended use of the computer1900 of this embodiment.

In an example, for communication between the computer 1900 and anexternal device or the like, the CPU 2000 executes a communicationprogram loaded on the RAM 2020 and instructs the communication interface2030 to perform communication processing based on processing detailsdescribed in the communication program.

The communication interface 2030 reads transmission data stored in atransmission buffer area or the like provided in a storage device, suchas the RAM 2020, the hard disk drive 2040, the flexible disk 2090, orthe CD-ROM 2095, under the control of the CPU 2000 and transmits thetransmission data to a network or writes reception data received fromthe network to a reception buffer area or the like provided in thestorage device.

The communication interface 2030 may transfer transmission/receptiondata to/from a storage device by direct memory access (DMA), asdescribed above, or alternatively, the CPU 2000 may transfertransmission/reception data by reading the data from the destinationcommunication interface 2030 or storage device and writing the data tothe destination communication interface 2030 or storage device.

Furthermore, the CPU 2000 reads all or necessary part of files ordatabases stored in external storage devices, such as the hard diskdrive 2040, the CD-ROM drive 2060 (CD-ROM 2095), and the flexible diskdrive 2050 (flexible disk 2090), into the RAM 2020 by DMA transfer orthe like and performs various processes on the data in the RAM 2020.

The CPU 2000 writes the processed data back to the external storagedevices by DMA transfer or the like. Since the RAM 2020 can be regardedas a temporary storage of the content in the external storage devices insuch processes, the RAM 2020 and the external storage devices arecollectively referred to as memories, storages, or storage devices inthis embodiment.

Various items of information in this embodiment, such as programs, data,tables, and databases, are stored on such storage devices and aresubjected to information processing. The CPU 2000 can also store part ofthe content of the RAM 2020 in a cache memory and can write and read thecache memory. Since the cache memory takes charge of part of thefunction of the RAM 2020 also in such a configuration, the cache memoryis also included in the RAM 2020, the memories, and/or the storagedevices in this embodiment except a case where it is distinguishedtherefrom.

Furthermore, the CPU 2000 performs, on data read from the RAM 2020,various processes including calculation, information processing,determination on conditions, search for information, and replacement ofinformation described in this embodiment, which are designated by aninstruction sequence of the program, and writes back the data to the RAM2020. For example, for the determination on conditions, the CPU 2000determines whether various variables shown in this embodiment satisfy acondition, such as being larger, smaller, equal to or greater than,equal to or less than, or equal to another variable or constant, and ifthe condition is satisfied (or not satisfied), the CPU 2000 goes to adifferent instruction sequence or calls a subroutine.

Furthermore, the CPU 2000 can search for information stored in files,databases, or the like in the storage devices. For example, in the casewhere a plurality of entries in which a second attribute value isassociated with a first attribute value are stored in a storage device,the CPU 2000 finds an entry whose first attribute value matches adesignated condition from among the plurality of entries stored in thestorage device and reads a second attribute value stored in the entry tothereby obtain a second attribute value associated with the firstattribute value that satisfies the predetermined condition.

The program or modules described above may be stored in an externalrecording medium. Examples of the recording medium include opticalrecording media, such as a DVD and a CD, a magneto optical recordingmedium, such as an MO, a tape medium, and a semiconductor memory, suchas an IC card, in addition to the flexible disk 2090 and the CD-ROM2095. The program may be provided to the computer 1900 via a networkusing a storage device, such as a hard disk and a RAM, provided in aserver system connected to a dedicated communication network or theInternet, as a recording medium.

Although the present invention has been described using embodiments, thetechnical scope of the present invention is not limited to the scope ofthe embodiments. It will be obvious to those skilled in the art thatvarious changes and modifications of the embodiments may be made. Itwill also be obvious from the scope of the invention that such changesand modifications are also included in the technical scope of thepresent invention.

It is to be understood that the processes, such as the operations,procedures, steps, and stages of the devices, systems, programs, andmethods shown in the scope of Claims, specification, and drawings, canbe achieved in any execution sequence, unless otherwise specified, suchas “before” or “prior to”, and unless an output of a previous process isused in the following process. Even if the scope of Claims, thespecification, and the operation procedure in the drawings are describedusing “first”, “second”, etc. for the purpose of convenience, it is notabsolutely necessary to execute the operation in this order.

What is claimed is:
 1. An information processing apparatus comprising: abehavior-history acquisition unit configured to acquire behaviorhistories of first users identified by first-user identificationinformation; a transmission-history acquisition unit configured toacquire information transmission histories of second users identified bysecond-user identification information; and a determination unitconfigured to determine identity between the first users and the secondusers on the basis of behavior details included in the behaviorhistories and transmission details included in the transmissionhistories.
 2. The information processing apparatus according to claim 1,wherein the information processing apparatus further comprises a memoryand a processor, wherein the acquired information transmission historiesof the second users is information acquired from a posted text, a textextracted from a posted sound, and text analyzed from a posted image,and wherein: the determination unit is further configured to determinethe identity between the first user and the second user on the basisincluding at least the behavior details, a plurality of degrees ofassociation for a plurality of combinations, transmission details, andon the basis of profile information on the first user and profileinformation on the second user, and determining the identity between thefirst user and the second user on the basis of the plurality of degreesof association includes calculating a degree of association for eachcombination of one or more behavior histories of the first user and oneor more transmission histories of the second user; and a display deviceis configured to display in response to determining the identity betweenthe first user and the second user, an advertisement to the second user.